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+output +data/cifar-10-batches-py/ +data/cifar-10-python.tar.gz +data/*.txt +data/*.list +data/mean.meta diff --git a/doc/fluid/new_docs/beginners_guide/basics/image_classification/README.cn.md b/doc/fluid/new_docs/beginners_guide/basics/image_classification/README.cn.md index 8d645718e12..4f20843596a 100644 --- a/doc/fluid/new_docs/beginners_guide/basics/image_classification/README.cn.md +++ b/doc/fluid/new_docs/beginners_guide/basics/image_classification/README.cn.md @@ -21,7 +21,7 @@ 图像分类包括通用图像分类、细粒度图像分类等。图1展示了通用图像分类效果,即模型可以正确识别图像上的主要物体。

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图1. 通用图像分类展示

@@ -30,7 +30,7 @@

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图2. 细粒度图像分类展示

@@ -38,7 +38,7 @@ 一个好的模型既要对不同类别识别正确,同时也应该能够对不同视角、光照、背景、变形或部分遮挡的图像正确识别(这里我们统一称作图像扰动)。图3展示了一些图像的扰动,较好的模型会像聪明的人类一样能够正确识别。

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图3. 扰动图片展示[22]

@@ -61,7 +61,7 @@ Alex Krizhevsky在2012年ILSVRC提出的CNN模型 \[[9](#参考文献)\] 取得了历史性的突破,效果大幅度超越传统方法,获得了ILSVRC2012冠军,该模型被称作AlexNet。这也是首次将深度学习用于大规模图像分类中。从AlexNet之后,涌现了一系列CNN模型,不断地在ImageNet上刷新成绩,如图4展示。随着模型变得越来越深以及精妙的结构设计,Top-5的错误率也越来越低,降到了3.5%附近。而在同样的ImageNet数据集上,人眼的辨识错误率大概在5.1%,也就是目前的深度学习模型的识别能力已经超过了人眼。

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图4. ILSVRC图像分类Top-5错误率

@@ -70,7 +70,7 @@ Alex Krizhevsky在2012年ILSVRC提出的CNN模型 \[[9](#参考文献)\] 取得 传统CNN包含卷积层、全连接层等组件,并采用softmax多类别分类器和多类交叉熵损失函数,一个典型的卷积神经网络如图5所示,我们先介绍用来构造CNN的常见组件。

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图5. CNN网络示例[20]

@@ -89,7 +89,7 @@ Alex Krizhevsky在2012年ILSVRC提出的CNN模型 \[[9](#参考文献)\] 取得 牛津大学VGG(Visual Geometry Group)组在2014年ILSVRC提出的模型被称作VGG模型 \[[11](#参考文献)\] 。该模型相比以往模型进一步加宽和加深了网络结构,它的核心是五组卷积操作,每两组之间做Max-Pooling空间降维。同一组内采用多次连续的3X3卷积,卷积核的数目由较浅组的64增多到最深组的512,同一组内的卷积核数目是一样的。卷积之后接两层全连接层,之后是分类层。由于每组内卷积层的不同,有11、13、16、19层这几种模型,下图展示一个16层的网络结构。VGG模型结构相对简洁,提出之后也有很多文章基于此模型进行研究,如在ImageNet上首次公开超过人眼识别的模型\[[19](#参考文献)\]就是借鉴VGG模型的结构。

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图6. 基于ImageNet的VGG16模型

@@ -106,7 +106,7 @@ NIN模型主要有两个特点: Inception模块如下图7所示,图(a)是最简单的设计,输出是3个卷积层和一个池化层的特征拼接。这种设计的缺点是池化层不会改变特征通道数,拼接后会导致特征的通道数较大,经过几层这样的模块堆积后,通道数会越来越大,导致参数和计算量也随之增大。为了改善这个缺点,图(b)引入3个1x1卷积层进行降维,所谓的降维就是减少通道数,同时如NIN模型中提到的1x1卷积也可以修正线性特征。

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图7. Inception模块

@@ -115,7 +115,7 @@ GoogleNet由多组Inception模块堆积而成。另外,在网络最后也没 GoogleNet整体网络结构如图8所示,总共22层网络:开始由3层普通的卷积组成;接下来由三组子网络组成,第一组子网络包含2个Inception模块,第二组包含5个Inception模块,第三组包含2个Inception模块;然后接均值池化层、全连接层。

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图8. GoogleNet[12]

@@ -130,14 +130,14 @@ ResNet(Residual Network) \[[15](#参考文献)\] 是2015年ImageNet图像分类 残差模块如图9所示,左边是基本模块连接方式,由两个输出通道数相同的3x3卷积组成。右边是瓶颈模块(Bottleneck)连接方式,之所以称为瓶颈,是因为上面的1x1卷积用来降维(图示例即256->64),下面的1x1卷积用来升维(图示例即64->256),这样中间3x3卷积的输入和输出通道数都较小(图示例即64->64)。

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图9. 残差模块

图10展示了50、101、152层网络连接示意图,使用的是瓶颈模块。这三个模型的区别在于每组中残差模块的重复次数不同(见图右上角)。ResNet训练收敛较快,成功的训练了上百乃至近千层的卷积神经网络。

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图10. 基于ImageNet的ResNet模型

@@ -149,7 +149,7 @@ ResNet(Residual Network) \[[15](#参考文献)\] 是2015年ImageNet图像分类 由于ImageNet数据集较大,下载和训练较慢,为了方便大家学习,我们使用[CIFAR10]()数据集。CIFAR10数据集包含60,000张32x32的彩色图片,10个类别,每个类包含6,000张。其中50,000张图片作为训练集,10000张作为测试集。图11从每个类别中随机抽取了10张图片,展示了所有的类别。

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图11. CIFAR10数据集[21]

@@ -377,7 +377,7 @@ test_reader = paddle.batch( `event_handler_plot`可以用来利用回调数据来打点画图:

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图12. 训练结果

@@ -469,7 +469,7 @@ Test with Pass 0, Loss 1.1, Acc 0.6 图13是训练的分类错误率曲线图,运行到第200个pass后基本收敛,最终得到测试集上分类错误率为8.54%。

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图13. CIFAR10数据集上VGG模型的分类错误率

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